Open Access

ARTICLE

DL-HAR: Deep Learning-Based Human Activity Recognition Framework for Edge Computing

by Abdu Gumaei, Mabrook Al-Rakhami, Hussain AlSalman, Sk. Md. Mizanur Rahman, Atif Alamri

1 Research Chair of Pervasive and Mobile Computing, King Saud University, Riyadh, 11543, Saudi Arabia.
2 College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.
3 Information and Communication Engineering Technology, School of Engineering Technology and Applied Science, Centennial College, Toronto, Canada.

* Corresponding Author: Abdu Gumaei. Email: .

Computers, Materials & Continua 2020, 65(2), 1033-1057. https://doi.org/10.32604/cmc.2020.011740

Received 27 May 2020; Accepted 03 July 2020; Issue published 20 August 2020

Abstract

Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them. Deep learning has gained momentum for identifying activities through sensors, smartphones or even surveillance cameras. However, it is often difficult to train deep learning models on constrained IoT devices. The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing, which we call DL-HAR. The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy. In order to evaluate the proposed framework, we conducted a comprehensive set of experiments to validate the applicability of DL-HAR. Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.

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Citations